Method for performing federated learning in wireless communication system, and apparatus therefor

ABSTRACT

Disclosed is a method by which a base station performs federated learning in a wireless communication system. A method, according to one embodiment of the present disclosure, comprises: receiving, by a base station, a plurality of local parameters from a plurality of terminals; obtaining an integrated parameter on the basis of the plurality of local parameters; and transmitting the integrated parameter to each of the plurality of terminals, wherein at least one bias included in the plurality of local parameters is removed by using the plurality of local parameters. The base station/terminals of the present disclosure may be linked to an artificial intelligence, module, an unmanned aerial vehicle (UAV), a robot, an augmented reality (AR) device, a virtual reality (VR) device, a device related to a 6G service, and the like.

TECHNICAL FIELD

The present disclosure relates to A method of performing federated learning and an apparatus for the same in a wireless communication system and, more particularly, to a method of transmitting and receiving data for federated learning between a base station and a terminal and an apparatus for the same.

BACKGROUND ART

A wireless communication system is widely deployed to provide various types of communication services such as voice and data. In general, a wireless communication system is a multiple access system capable of supporting communication with multiple users by sharing available system resources (bandwidth, transmission power, etc.). Examples of the multiple access system include a Code Division Multiple Access (CDMA) system, a Frequency Division Multiple Access (FDMA) system, a Time Division Multiple Access (TDMA) system, a Space Division Multiple Access (SDMA) system, and an Orthogonal Frequency Division Multiple Access (OFDMA) system, SC-FDMA (Single Carrier Frequency Division Multiple Access) system, IDMA (Interleave Division Multiple Access) system, and the like.

Meanwhile, the attempt to integrate the artificial intelligence (AI) technology into a wireless communication system are abruptly increased. An example of the AI integrated scheme may be divided into the communications for AI (C4AI), which improves the communication technology for supporting AI, and the AI for communications (AIC), which utilizes AI to improve the communication performance.

In the AI4C region, there is an attempt to improve a design efficiency by replacing a channel encoder/decoder with an autoencoder of end-to-end. In the C4AI region, there is federated learning (FL), a technique of distributed learning, a method of updating a common predictive model while protecting personal information by sharing only weights or gradients of the model with a server without sharing data with a device. There are also technologies that distribute load of a device, a network edge, and a cloud server with split interference.

DISCLOSURE Technical Problem

The present disclosure is provided to solve the requirement and/or problem described above.

Furthermore, the present disclosure is to implement A method of performing federated learning and an apparatus for the same for a data communication of lowered delay between a base station and a terminal in a wireless communication system.

Technical Solution

According to an embodiment of the present disclosure, A method of performing a federated learning in a wireless communication system, the method performed by a base station comprising: receiving, from a plurality of terminals, a plurality of local parameters; obtaining an integrated parameter based on the plurality of local parameters; and transmitting, to the plurality of terminals, the integrated parameter, wherein obtaining the integrated parameter includes: removing at least one bias included in the plurality of local parameters by using the plurality of local parameters.

Furthermore, wherein the at least one bias is determined based on a sign of a gradient of the local parameter in which each of the at least one bias is included, wherein obtaining the integrated parameter includes: determining a sign value of each of a plurality of gradients included in the plurality of local parameters by using at least one removal parameter included in the plurality of local parameters, and wherein removing the at least one bias includes removing the at least one bias based on a sign value of each of the plurality of gradients.

Furthermore, wherein determining the sign value includes: determining the sign value of each of the plurality of gradients based on a number of at least one removal parameters.

Furthermore, wherein determining the sign value includes: determining the number of at least one removal parameters based on a number of local parameters having a positive value among the plurality of local parameters.

Furthermore, wherein the at least one parameter is determined based on at least one of a number of the plurality of terminals and a transmission power of the plurality of terminals.

According to another embodiment of the present disclosure, a base station for performing a federated learning in a wireless communication system, comprising: a communication module configured to receive a plurality of local parameters from a plurality of terminals; and a processor configured to obtain an integrated parameter based on the plurality of local parameters and transmitting the integrated parameter to the plurality of terminals, wherein the processor is configured to remove at least one bias included in the plurality of local parameters by using the plurality of local parameters.

Furthermore, wherein the at least one bias is determined based on a sign of a gradient of the local parameter in which each of the at least one bias is included, wherein the processor is configured to: determine a sign value of each of a plurality of gradients included in the plurality of local parameters by using at least one removal parameter included in the plurality of local parameters, and remove the at least one bias based on a sign value of each of the plurality of gradients.

Furthermore, wherein the processor is configured to: determine the sign value of each of the plurality of gradients based on a number of at least one removal parameters.

Furthermore, wherein the processor is configured to: determine the number of at least one removal parameters based on a number of local parameters having a positive value among the plurality of local parameters.

Furthermore, wherein the at least one parameter is determined based on at least one of a number of the plurality of terminals and a transmission power of the plurality of terminals.

According to another embodiment of the present disclosure, a base station comprising: one or more transceivers; one or more processors; and one or more memories connected to the one or more processors and configured to store instructions, wherein the instructions are executed by the one or more processors such that the one or more processors support operations for the base station to perform a federated learning, wherein the operations include: receiving, from a plurality of terminals, a plurality of local parameters; obtaining an integrated parameter based on the plurality of local parameters; and transmitting, to the plurality of terminals, the integrated parameter, wherein obtaining the integrated parameter includes: removing at least one bias included in the plurality of local parameters by using the plurality of local parameters.

Advantageous Effects

The technical effect is described for A method of performing federated learning and an apparatus for the same in a wireless communication system according to an embodiment of the present disclosure as below.

The present disclosure may solve the ambiguity problem which inevitably occurs when a bias is applied to utilize a real number value range, not a positive value range, in the AirComp technique.

In addition, according to the present disclosure, a width of distribution of a reception signal in a data communication may be narrowed.

In addition, according to the present disclosure, a power increase gain is increased in comparison with that of the conventional art, and an MSE value may be decreased.

In addition, according to the present disclosure, an approximate average power is used in the same transmission signal size range in comparison with that of the conventional art, and a reception reliability may be improved.

In addition, according to the present disclosure, the number of terminals able to participate in federated learning under a given reliability condition is increased, and a learning delay speed may be significantly reduced.

The effects to be achieved by the present disclosure are not limited to the above-mentioned effects, and other effects not mentioned herein may be clearly understood by those skilled in the art from the following description.

DESCRIPTION OF DRAWINGS

The accompanying drawings, which are included to provide a further understanding of the present disclosure and constitute a part of the detailed description, illustrate embodiments of the present disclosure and together with the description serve to explain the principle of the present disclosure.

FIG. 1 illustrates physical channels and general signal transmission used in a 3GPP system.

FIG. 2 is a diagram illustrating an example of a communication structure providable in a 6G system.

FIG. 3 illustrates a perceptron structure.

FIG. 4 illustrates a multi-perceptron structure.

FIG. 5 illustrates a deep neural network structure.

FIG. 6 illustrates a convolutional neural network structure.

FIG. 7 illustrates a filter operation in a convolutional neural network.

FIG. 8 illustrates a neural network structure in which a recurrent loop is present.

FIG. 9 illustrates an operating structure of a recurrent neural network.

FIG. 10 illustrates an example of an electromagnetic spectrum.

FIG. 11 illustrates an example of THz communication application.

FIG. 12 is a diagram schematically illustrating a federated learning process according to an embodiment of the present disclosure.

FIG. 13 is a flowchart illustrating a federated learning method of a BS in a wireless communication system according to an embodiment of the present disclosure.

FIG. 14 is a diagram illustrating a change of gradient value according to an embodiment of the present disclosure.

FIGS. 15 and 16 are graphs illustrating the technical effect of the federated learning method according to an embodiment of the present disclosure.

FIG. 17 illustrates a communication system applied to the present disclosure.

FIG. 18 illustrates a wireless device that can be applied to the present disclosure.

FIG. 19 illustrates a signal processing circuit for a transmission signal.

FIG. 20 shows another example of a wireless device applied to the present disclosure.

FIG. 21 illustrates a portable device applied to the present disclosure.

FIG. 22 illustrates a vehicle or an autonomous vehicle to which the present disclosure is applied.

FIG. 23 illustrates a vehicle applied to the present disclosure.

FIG. 24 illustrates an XR device applied to the present disclosure.

FIG. 25 illustrates a robot applied to the present disclosure.

FIG. 26 illustrates an AI device applied to the present disclosure.

MODE FOR DISCLOSURE

Hereinafter, an embodiment disclosed in the present disclosure will be described in detail with reference to the accompanying drawings and the same or similar components are denoted by the same reference numerals regardless of a sign of the drawing, and duplicated description thereof will be omitted. Suffixes “module” and “unit” for components used in the following description are given or mixed in consideration of easy preparation of the present disclosure only and do not have their own distinguished meanings or roles. Further, in describing an embodiment disclosed in the present disclosure, a detailed description of related known technologies will be omitted if it is determined that the detailed description makes the gist of the embodiment of the present disclosure unclear. Further, it is to be understood that the accompanying drawings are just used for easily understanding the embodiments disclosed in the present disclosure and a technical spirit disclosed in the present disclosure is not limited by the accompanying drawings and all changes, equivalents, or substitutes included in the spirit and the technical scope of the present disclosure are included.

Terms including an ordinary number, such as first and second, are used for describing various elements, but the elements are not limited by the terms. The terms are used only to discriminate one element from another element.

It should be understood that, when it is described that a component is “connected to” or “accesses” another component, the component may be directly connected to or access the other component or a third component may be present therebetween. In contrast, when it is described that a component is “directly connected to” or “directly accesses” another component, it is understood that no element is present between the element and another element.

A singular form includes a plural form if there is no clearly opposite meaning in the context.

In the present application, it should be understood that term “include” or “have”indicates that a feature, a number, a step, an operation, a component, a part or the combination thereof described in the present disclosure is present, but does not exclude a possibility of presence or addition of one or more other features, numbers, steps, operations, components, parts or combinations thereof, in advance.

The following technology may be used in various radio access system including CDMA, FDMA, TDMA, OFDMA, SC-FDMA, and the like. The CDMA may be implemented as radio technology such as Universal Terrestrial Radio Access (UTRA) or CDMA2000. The TDMA may be implemented as radio technology such as a global system for mobile communications (GSM)/general packet radio service (GPRS)/enhanced data rates for GSM evolution (EDGE). The OFDMA may be implemented as radio technology such as Institute of Electrical and Electronics Engineers (IEEE) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, Evolved UTRA (E-UTRA), or the like. The UTRA is a part of Universal Mobile Telecommunications System (UMTS). 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) is a part of Evolved UMTS (E-UMTS) using the E-UTRA and LTE-Advanced (A)/LTE-A pro is an evolved version of the 3GPP LTE. 3GPP NR (New Radio or New Radio Access Technology) is an evolved version of the 3GPP LTE/LTE-A/LTE-A pro. 3GPP 6G may be an evolved version of 3GPP NR.

For clarity of description, the technical spirit of the present disclosure is described based on the 3GPP communication system (e.g., LTE or NR etc), but the technical spirit of the present disclosure are not limited thereto. LTE means technology after 3GPP TS 36.xxx Release 8. In detail, LTE technology after 3GPP TS 36.xxx Release 10 is referred to as the LTE-A and LTE technology after 3GPP TS 36.xxx Release 13 is referred to as the LTE-A pro. The 3GPP NR means technology after TS 38.xxx Release 15. 3GPP 6G may mean technology after TS Release 17 and/or Release 18. “xxx” means standard document detail number. LTE/NR/6G may be collectively referred to as a 3GPP system. For background art, terms, abbreviations, etc. used in the description of the present disclosure, reference may be made to matters described in standard documents published prior to the present disclosure. For example, the following document can be referred to.

3GPP LTE

-   -   36.211: Physical channels and modulation     -   36.212: Multiplexing and channel coding     -   36.213: Physical layer procedures     -   36.300: Overall description     -   36.331: Radio Resource Control (RRC)

3GPP NR

-   -   38.211: Physical channels and modulation     -   38.212: Multiplexing and channel coding     -   38.213: Physical layer procedures for control     -   38.214: Physical layer procedures for data     -   38.300: NR and NG-RAN Overall Description     -   38.331: Radio Resource Control (RRC) protocol specification

Physical Channels and Frame Structure Physical Channel and General Signal Transmission

FIG. 1 illustrates physical channels and general signal transmission used in a 3GPP system. In a wireless communication system, the UE receives information from the eNB through Downlink (DL) and the UE transmits information from the eNB through Uplink (UL). The information which the eNB and the UE transmit and receive includes data and various control information and there are various physical channels according to a type/use of the information which the eNB and the UE transmit and receive.

When the UE is powered on or newly enters a cell, the UE performs an initial cell search operation such as synchronizing with the eNB (S11). To this end, the UE may receive a Primary Synchronization Signal (PSS) and a (Secondary Synchronization Signal (SSS) from the eNB and synchronize with the eNB and acquire information such as a cell ID or the like. Thereafter, the UE may receive a Physical Broadcast Channel (PBCH) from the eNB and acquire in-cell broadcast information. Meanwhile, the UE receives a Downlink Reference Signal (DL RS) in an initial cell search step to check a downlink channel status.

A UE that completes the initial cell search receives a Physical Downlink Control Channel (PDCCH) and a Physical Downlink Control Channel (PDSCH) according to information loaded on the PDCCH to acquire more specific system information (S12).

Meanwhile, when there is no radio resource first accessing the eNB or for signal transmission, the UE may perform a Random Access Procedure (RACH) to the eNB (S13 to S16). To this end, the UE may transmit a specific sequence to a preamble through a Physical Random Access Channel (PRACH) (S13 and S15) and receive a response message (Random Access Response (RAR) message) for the preamble through the PDCCH and a corresponding PDSCH. In the case of a contention based RACH, a Contention Resolution Procedure may be additionally performed (S16).

The UE that performs the above procedure may then perform PDCCH/PDSCH reception (S17) and Physical Uplink Shared Channel (PUSCH)/Physical Uplink Control Channel (PUCCH) transmission (S18) as a general uplink/downlink signal transmission procedure. In particular, the UE may receive Downlink Control Information (DCI) through the PDCCH. Here, the DCI may include control information such as resource allocation information for the UE and formats may be differently applied according to a use purpose.

Meanwhile, the control information which the UE transmits to the eNB through the uplink or the UE receives from the eNB may include a downlink/uplink ACK/NACK signal, a Channel Quality Indicator (CQI), a Precoding Matrix Index (PMI), a Rank Indicator (RI), and the like. The UE may transmit the control information such as the CQI/PMI/RI, etc., through the PUSCH and/or PUCCH.

Structures of Uplink and Downlink Channels Downlink Channel Structure

The BS transmits an associated signal to the UE through a downlink channel to be described below and the UE receives the associated signal from the BS through the downlink channel to be described below.

(1) Physical Downlink Shared Channel (PDSCH)

The PDSCH transports downlink data (e.g., DL-shared channel transport block (DL-SCH TB)), and adopts modulation methods such as Quadrature Phase Shift Keying (QPSK), 16 Quadrature Amplitude Modulation (QAM), 64 QAM, and 256 QAM. A codeword is generated by encoding a TB. The PDSCH may carry multiple codewords. Scrambling and modulation mapping are performed for each codeword and modulation symbols generated from each codeword are mapped to one or more layers (layer mapping). Each layer is mapped to a resource together with a demodulation reference signal (DMRS), generated as an OFDM symbol signal, and transmitted through a corresponding antenna port.

(2) Physical Downlink Control Channel (PDCCH)

The PDCCH transports downlink control information (DCI) and a QPSK modulation method is applied. One PDCCH is constituted by 1, 2, 4, 8, and 16 Control Channel Elements (CCEs) according to an Aggregation Level (AL). One CCE is constituted by 6 Resource Element Groups (REGs). One REG is defined by one OFDM symbol and one (P)RB.

The UE performs decoding (so-called, blind decoding) for a set of PDCCH candidates to obtain the DCI transmitted through the PDCCH. The set of PDCCH candidates decoded by the UE is defined as a PDCCH search space set. The search space set may be a common search space or a UE-specific search space. The UE may obtain the DCI by monitoring PDCCH candidates in one or more search space sets configured by the MIB or higher layer signaling.

Uplink Channel Structure

The UE transmits an associated signal to the BS through an uplink channel to be described below and the BS receives the associated signal from the UE through the uplink channel to be described below.

(1) Physical Uplink Shared Channel (PUSCH)

The PUSCH transports uplink data (e.g., UL-shared channel transport block (UL-SCH TB) and/or uplink control information (UCI) and is transmitted based on a Cyclic Prefix-Orthogonal Frequency Division Multiplexing (CP-OFDM) waveform or a Discrete Fourier Transform-spread-Orthogonal Frequency Division Multiplexing (DFT-s-OFDM) waveform. When the PUSCH is transmitted based on the DFT-s-OFDM waveform, the UE transmits the PUSCH by applying transform precoding. As an example, when the transform precoding is disable (e.g., transform precoding is disabled), the UE transmits the PUSCH based on the CP-OFDM waveform, and when the transform precoding is enabled (e.g., transform precoding is enabled), the UE may transmit the PUSCH based on the CP-OFDM waveform or the DFT-s-OFDM waveform. PUSCH transmission is dynamically scheduled by the UL grant in the DCI or semi-statically scheduled based on higher layer (e.g., RRC) signaling (and/or Layer 1 (L1) signaling (e.g., PDCCH)) (configured grant). The PUSCH transmission may be performed based on a codebook or a non-codebook.

(2) Physical Uplink Control Channel (PUCCH)

The PUCCH may transport uplink control information, HARQ-ACK, and/or scheduling request (SR), and may be divided into multiple PUCCHs according to a PUCCH transmission length.

General Contents of 6G System

A 6G (wireless communication) system has purposes including (i) a very high data rate per device, (ii) a very large number of connected devices, (iii) global connectivity, (iv) a very low latency, (v) reduction of energy consumption of battery-free IoT devices, (vi) ultrahigh reliability connection, (vii) connected intelligence having a machine learning capability, etc. A vision of the 6G system may be four aspects, e.g., intelligent connectivity, deep connectivity, holographic connectivity, and ubiquitous connectivity, and the 6G system may satisfy requirements shown in Table 1 below. That is, Table 1 shows an example of the requirements of the 6G system.

TABLE 1 Per device 1 Tbps peak data rate E2E latency 1 ms Maximum 100 bps/Hz spectral efficiency Mobility Up to support 1000 km/hr Satellite Fully integration Al Fully Autonomous Fully vehicle XR Fully Haptic Fully Communication

The 6G system may have key factors such as Enhanced mobile broadband (eMBB), Ultra-reliable low latency communications (URLLC), massive machine-type communication (mMTC), AI integrated communication, Tactile internet, High throughput, High network capacity, High energy efficiency, Low backhaul and access network congestion, and Enhanced data security.

FIG. 2 is a diagram illustrating an example of a communication structure providable in a 6G system.

It is anticipated that the 6G system has higher simultaneous wireless communication connectivity which is 50 times higher than the 5G wireless communication system. The URLLC which is the key feature of 5G will be a more important technology by providing a smaller end-to-end latency than 1 ms in the 6G communication. In the 6G system, volume spectrum efficiency will be even more excellent unlike region spectrum efficiency frequently used. The 6G system may provide an advanced battery technology for a very long battery life-span and energy harvesting. In 6G, new network features may be as follows.

-   -   Satellites integrated network: It is anticipated that the 6G         will be integrated with the satellite in order to provide a         global mobile group. Integrating the terrestrial, the satellite,         and the aerial network into one wireless communication system is         very important for the 6G.     -   Connected intelligence: Unlike the wireless communication system         of the previous generation, the 6G is innovative, and wireless         evolution will be updated from “connected things” to “connected         intelligence”. The AI may be applied in each step (or each         procedure of signal processing to be described below) of a         communication procedure.     -   Seamless integration wireless information and energy transfer:         The 6G wireless network will transfer power in order to charge         batteries of devices like smartphones and sensors. Therefore,         wireless information and energy transmission (WIET) will be         integrated.     -   Ubiquitous super 3D connectivity: Connection to networks of         networks and core network functions of a drone and a very low         earth orbit network will make a super 3D connection in 6G         ubiquitous.

Several general requirements may be as follows in new network features of the 6G described above.

-   -   Small cell networks: An ID of the small cell network is         introduced in order to enhance a received signal quality as a         result of enhancement of a throughput, energy efficiency and         spectrum efficiency in a cellular system. Consequently, the         small cell network is a required feature of communication         systems of 6G and beyond 5G (5GB) or more. Therefore, the 6G         communication system also adopts the feature of the small cell         network.     -   Ultra-dense heterogeneous network: Ultra-dense heterogeneous         networks will become another important feature of the 6G         communication system. A multi-tier network constituted by         heterogeneous networks improves entire QoS and reduces cost.     -   High-capacity backhaul: The backhaul connectivity is         characterized as the high-capacity backhaul network in order to         support high-capacity traffic. A high-speed optical fiber and         free space optic system may be an available solution for such a         problem.     -   Radar technology integrated with mobile technology:         High-precision localization (location based service) through         communication is one of the functions of the 6G wireless         communication system. Therefore, a radar system will be         integrated with the 6G network.     -   Softwarization and virtualization: The softwarization and         virtualization are two important functions which become a basis         of a design process in the 5GB network in order to guarantee         reconfigurability and programmability. Further, in a sharing         physical infrastructure, billions of devices may be shared.

Core Implementation Technology of 6G System Artificial Intelligence

The AI is most important in the 6G system, and is a technology to be newly introduced. The AI is not involved in the 4G system. The 5G system will support a partial or very limited AI. However, the 6G system will completely support the AI for automation. Development of machine learning will make a more intelligent network for real-time communication in the 6G. When the AI is introduced into communication, real-time data transmission may be simplified and enhanced. The AI may determine a scheme in which a complicated target task is performed by using numerous analyses. That is, the AI may increase efficiency and decrease the processing latency.

Time-consuming task such as handover, network selection, and resource scheduling may be immediately performed by using the AI. The AI may play an important role even in M2M, machine-to-human, and human-to-machine communication. Further, the AI may become rapid communication in a brain computer interface (BCI). An AI based communication system may be supported by a metal material, an intelligent structure, an intelligent network, an intelligent device, an intelligent cognition radio, a self-maintenance network, and machine learning.

In recent years, an attempt to integrate the AI with the wireless communication system has appeared, but this concentrates an application layer and a network layer, in particular, deep learning on a wireless resource management and allocation field. However, this study is gradually developed to a MAC layer and a physical layer, and in particular, there are attempts to combine the deep learning with wireless transmission in a physical layer. AI based physical layer transmission means not a traditional communication framework, but a signal processing and communication mechanism based on an AI driver in a fundamental signal processing and communication mechanism. For example, the AI based physical layer transmission may include channel coding and decoding, deep learning based signal estimation and detection, a deep learning based MIMI mechanism, AI based resource scheduling and allocation, etc.

The machine learning may be used for channel estimation and channel tracking, and used for power allocation, interference cancellation, etc., in a downlink physical layer. Further, the machine learning may also be used for antenna selection, power control, symbol detection, etc., in an MIMO system.

However, application of DNN for transmission in the physical layer may have the following problem.

In a deep learning based AI algorithm, numerous training data are required to optimize training parameters. However, due to a limit in acquiring data in a specific channel environment as the training data, a lot of training data are used offline. This, in the specific channel environment, with respect to static training for the training data, a contradiction may occur between dynamic features and diversity of a radio channel.

Further, current deep learning primarily targets an actual real signal. However, signals of the physical layer of the wireless communication are complex signals. In order to match features of wireless communication signals, a study for a neural network which detects a complex domain signal is further required.

Hereinafter, the machine learning will be described in more detail.

The machine learning means a series of operations of training the machine in order to make a machine which may perform a task which a person may perform or which it is difficult for the person to perform. Data and a learning model are required for the machine learning. A learning method of the data in the machine learning may be generally classified into three, i.e., supervised learning, unsupervised learning, and reinforcement learning.

Learning of a neural network is to minimize an error of an output. The learning of the neural network is a process of repeatedly inputting learning data into the neural network and calculating the output of the neural network for the learning data and the error of a target and back-propagating the errors of the neural network from the output layer of the neural network toward the input layer in a direction to reduce the errors to update the weight of each node of the neural network.

The supervised learning may use learning data in which the learning data is labeled with a correct answer, and the unsupervised learning may use learning data in which not be labeled with the correct answer. That is for example, the learning data in the case of the supervised learning related to the data classification may be data in which category is labeled in each learning data. The labeled learning data is input to the neural network, and the error may be calculated by comparing the output (category) of the neural network with the label of the learning data. The calculated error is back-propagated in a reverse direction (i.e., a direction from the output layer toward the input layer) in the neural network and connection weights of respective nodes of each layer of the neural network may be updated according to the back propagation. A variation amount of the updated connection weight of each node may be determined according to a learning rate. Calculation of the neural network for the input data and the back-propagation of the error may constitute a learning cycle (epoch). A learning rate may be applied differently according to the number of repetition times of the learning cycle of the neural network. For example, in an initial stage of the learning of the neural network, the neural network ensures a certain level of performance quickly by using a high learning rate, thereby increasing efficiency and a low learning rate is used in a latter stage of the learning, thereby increasing accuracy.

The learning method may vary depending on a feature of data. For example, when a purpose is to accurately predict data by a transmitter by a receiver on the communication system, it is more preferable to perform learning by using the supervised learning than the unsupervised learning or reinforcement learning.

The learning model corresponds to a human brain, and a most basic linear model may be considered, but a paradigm of a machine learning a neural network structure having high complexity such as artificial neural networks as the learning model is referred to the deep learning.

A neural network core used as the learning scheme generally includes deep neural networks (DNN), convolutional deep neural networks (CNN), and recurrent neural networks (RNN) (recurrent Boltzman machine) schemes.

The artificial neural network is an example of connecting several perceptrons.

Referring to FIG. 3 , when an input vector x=(18, 19, . . . , xd) is input, an entire process of multiplying each component by weights (17, W2, . . . , Wd), and aggregating all of the results are aggregated, and then applying an active function σ(⋅) is referred to as perceptron. A massive artificial neural network structure extends a simplified perceptron structure illustrated in FIG. 3 to apply the input vector to different multi-dimension perceptrons. For convenience of description, an input value or an output value is referred to as a node.

Meanwhile, it may be described that the perceptron structure illustrated in FIG. 3 is constituted by a total of three layers based on the input value and the output value. An artificial neural network in which there are H (d+1)-dimension perceptrons between a 1^(st) layer and a 2^(nd) layer and K (H+1)-dimension perceptrons between the 2^(nd) layer and a 3^(rd) layer may be expressed as in FIG. 4 .

An input layer on which the input vector is located is referred to as an input layer, an output layer on which a final output value is located is referred to as an output layer, and all layers located between the input layer and the output layer are referred to as a hidden layer. In the illustration of FIG. 4 , three layers are disclosed, but when the actual number of artificial neural network layers is counted, the number is counted except for the input layer, so the actual number of artificial neural network layers may be regarded as a total of two layers. The artificial neural network is configured by connecting a perceptron of a basic block in two dimensions.

The input layer, hidden layer, and output layer may be commonly applied in various artificial neural network structures such as CNN, RNN, etc., to be described below in addition to a multi-layer perceptron. As the number of hidden layers increases, the artificial neural network is deeper, and a machine learning paradigm that uses a sufficiently deepened artificial neural network as the learning model is referred to as deep learning. Further, the artificial neural network used for the deep learning is referred to as a deep neural network (DNN).

The DNN illustrated in FIG. 5 is a multi-layer perceptron constituted by eight hidden layer+output layer. The multi-layer perceptron structure is expressed as a fully-connected neural network. In the fully-connected neural network, there is no connection relationship between nodes located on the same layer, and there is the connection relationship only between nodes located on adjacent layers. The DNN has a fully-connected neural network structure, and is configured by a combination of multiple hidden layers and active functions, and may be usefully applied to determining correlation features between an input and an output. Her, the correlation features may mean a joint probability of the input and the output.

Meanwhile, various artificial neural network structures different from the DNN may be formed according to how a plurality of perceptrons being connected to each other.

In the DNN, nodes located inside one layer are disposed in a 1-dimensional vertical direction. However, in FIG. 6 , a case where w horizontal nodes and h vertical nodes are disposed in two dimensions with respect to the nodes may be assumed (a convolutional neural network structure of FIG. 6 ). In this case, since a weight is added per one connection in a connection process connected from one input node to the hidden layer, a total of h×w weights should be considered. Since there are h×w nodes in the input layer, a total of h2w2 weights are required between two adjacent layers.

The convolutional neural network of FIG. 6 has a problem in that the number of weights exponentially increases according to the number of connections, so by assuming that there is a small-sized filter instead of considering all mode connections between the adjacent layers, a weighted sum and an active function operation are performed with respect to a portion where the filters are overlapped as illustrated I FIG. 7 .

One filter has weights corresponding to a number as large as a size thereof, and the weight may be learned so as to extract and output any specific feature on the image as a factor. In FIG. 7 , a 3×3-sized filter is applied to an uppermost left end 3×3 region of the input layer, and an output value of a result of performing the weighted sum and the active function operation for the corresponding node is stored in z22.

The filter performs the weighted sum and active function operation while moving at predetermined horizontal and vertical intervals while scanning the input layer, and the output value thereof is located at a position of a current filter. Such an operation scheme is similar to a conventional operation for an image in a computer vision field, so the DNN having such a structure is referred to as a convolutional neural network (CNN) and a hidden layer generated as the result of the convolutional operation is referred to as a convolutional layer. Further, a neural network in which there is a plurality of convolutional layers is referred to as a deep convolutional neural network (DCNN).

In the convolutional layer, in the node at which the current filter is located, only a node located at a region covered by the filter is included to calculate the weighted sum, thereby reducing the number of weights. As a result, one filter may be used to concentrate on a feature for a local region. As a result, the CNN may be effectively applied to image data processing in which a physical distance in a 2D region becomes an important determination criterion. Meanwhile, in the CNN, a plurality of filters may be applied just before the convolutional layer, and a plurality of output results may also be generated through the convolutional operation of each filter.

Meanwhile, there may be data in which sequence features are important. A structure applying a scheme of inputting one element on a data sequence every timestep by considering a length variability and a time order of the sequence data, and inputting an output vector (hidden vector) of the hidden layer output at a specific time jointly with a just next element on the sequence to the artificial neural network is referred to a recurrent neural network structure.

Referring to FIG. 8 , the recurrent neural network (RNN) is a structure of applying the weighted sum and the active function by jointly inputting hidden vectors (z1(t−1), z2(t−1), . . . , zH(t−1)) at an immediately previous time t−1 in the process of elements (18(t), 19(t), . . . , xd(t)) at any time t on the data sequence. A reason for transferring the hidden vector to a subsequent time is that it is regarded that information in the input vector at the previous times is accumulated in the hidden vector at the current time.

Referring to FIG. 8 , the RNN operates according to a predetermined time order with respect to the input data sequence.

A hidden vector (z1(1), z2(1), . . . , zH(1)) when an input vector (18(t), 19(t), . . . , xd(t)) at time 1 is input into the RNN is input jointly with an input vector (18(2), 19(2), . . . , xd(2)) at time 2 to determine a vector (z1(2), z2(2), . . . , zH(2)) of the hidden layer through the weighted sum and the active function. Such a process is repeatedly performed up to time 2, time 3, . . . , time T.

Meanwhile, when a plurality of hidden layers is disposed in the RNN, this is referred to as a deep recurrent neural network (DRNN). The RNN is usefully applied to the sequence data (e.g., natural language processing) and designed.

The neural network core used as the learning scheme may include various deep learning techniques such as a Restricted Boltzman Machine (RBM), deep belief networks (DBN), and a Deep Q-Network in addition to the DNN, the CNN, and the RNN, and may be applied to fields such as computer vision, voice recognition, natural language processing, voice/signal processing, etc.

In recent years, an attempt to integrate the AI with the wireless communication system has appeared, but this concentrates an application layer and a network layer, in particular, deep learning on a wireless resource management and allocation field. However, this study is gradually developed to a MAC layer and a physical layer, and in particular, there are attempts to combine the deep learning with wireless transmission in a physical layer. AI based physical layer transmission means not a traditional communication framework, but a signal processing and communication mechanism based on an AI driver in a fundamental signal processing and communication mechanism. For example, the AI based physical layer transmission may include channel coding and decoding, deep learning based signal estimation and detection, a deep learning based MIMI mechanism, AI based resource scheduling and allocation, etc.

Terahertz (THz) Communication

The data rate may increase a bandwidth. This may be performed by using sub-THz communication with a wide bandwidth, and applying an advanced massive MIMO technology. A THz wave also known as a radiation of millimeters or less generally shows a frequency band between 0.1 THz and 10 THz having a corresponding wavelength in the range of 0.03 mm to 3 mm. A 100 GHz to 300 GHz band range (sub THz band) is regarded as a primary part of the THz for cellular communication. When the sub-THz band is added to a mmWave band, a 6G cellular communication capacity increases. 300 GHz to 3 THz in a defined THz band is present in an infrared (IR) frequency band. The 300GHz to 3 THz band is a part of a wideband, but is present on a boundary of the wideband, and is present just behind an RF band. Therefore, the 300 GHz to 3 THz band shows a similarity to the RF. FIG. 10 illustrates an example of an electromagnetic spectrum.

The THz wave may be located between the radio frequency (RF)/millimeter (mm), and the IR band, and (i) may better transmit a non-metallic/non-polarizable material than visible light/infrared rays, and have a smaller wavelength than the RF/millimeter wave, and has a high straightness, and may be capable of focusing a beam. Further, since photon energy of the THz wave is only a few meV, the photon energy is harmless to the human body. A frequency band which is expected to be used for the THz wireless communication may be a D-band (110 GHz to 170 GHz) or an H-band (220 GHz to 325 GHz) having small radio wave loss by molecular absorption in the air. The standardization of the THz wireless communication is discussed around the IEEE 802.15 THz working group in addition to 3GPP, and a standard document issued by the IEEE 802.15 Task Group (TG3d, TG3e) may embody or supplement the contents described in the present disclosure. The THz wireless communication may be applied in wireless cognition, sensing, imaging, wireless communication, THz navigation, etc. Primary features of the THz communication include (i) a bandwidth widely usable to support a very high data rate, and (ii) a high path loss generated at a high frequency (a high directional antenna is required). A narrow beam width generated by the high directional antenna reduces interference. A small wavelength of the THz signal may allow even more antenna elements to be integrated into a device and a BS which operate in this band. Through this, an advanced adaptive arrangement technology capable of range limitation may be used.

FIG. 11 illustrates an example of THz communication application.

As illustrated in FIG. 11 , a THz wireless communication scenario may be categorized into a macro network, a micro network, and a nanoscale network. In the macro network, the THz wireless communication may be applied to a vehicle-to-vehicle connection and a backhaul/fronthaul connection. In the micro network, the THz wireless communication may be applied to an indoor small cell, a fixed point-to-point or multi-point connection such as wireless connection in a data center, and near-field communication such as kiosk downloading.

Table 2 below is a table that shows an example a technology which may be used in the THz wave.

TABLE 2 Transceivers Device Available immature: UTC-PD, RTD and SBD Modulation and coding Low order modulation techniques (OOK, QPSK), LDPC, Reed Soloman, Hamming, Polar, Turbo Antenna Omni and Directional, phased array with low number of antenna elements Bandwidth 69 GHz (or 23 GHZ) at 300 GHz Channel models Partially Data rate 100 Gbps Outdoor deployment No Free space loss High Coverage Low Radio Measurements 300 GHz indoor Device size Few micrometers

Optical Wireless Technology

An OWC technology is planed for 6G communication in addition to RF based communication for all available device-to-access networks. The network is connected to a network-to-backhaul/fronthaul network connection. The OWC technology is already used after the 4G communication system, but may be more widely used for meeting the requirements of the 6G communication system. The OWC technologies such as light fidelity, visible light communication, optical commerce communication, and FSO communication based on the wideband are already well known technologies. The optical wireless technology based communication may provide a very high data speed, a low latency time, and safe communication. LiDAR may also be used for superultra high resolution 3D mapping in the 6G communication based on the wideband.

FSO Backhaul Network

Transmitter and receiver features of the FSO system are similar to the features of the optical fiber network. Therefore, data transmission of the FSO system is similar to that of the optical fiber system. Therefore, the FSO may become an excellent technology that provides the backhaul connection in the 6G system jointly with the optical fiber network. When the FSO is used, very long-distance communication is enabled even in a distance of 10000 km or more. The FSO supports a massive backhaul connection for remote and non-remote regions such as the seat, the space, the underwater, and an isolated island. The FSO also supports a cellular BS connection.

Massive MIMO Technology

One of the core technologies for enhancing the spectrum efficiency is application of the MIMO technology. When the MIMO technology is enhanced, the spectrum efficiency is also enhanced. Therefore, the massive MIMO technology will be important in the 6G system. Since the MIMO technology uses multiple paths, a multiplexing technology and a beam generation and operating technology suitable for the THz band should also be considered importantly so that the data signal is transmitted through one or more paths.

Blockchain

The blockchain will become an important technology for managing mass data in a future communication system. The blockchain is a form of a distributed ledger technology, and the distributed ledger is a database distributed in numerous nodes or computing devices. Each node replicates and stores the same ledger copy. The blockchain is managed by a P2P network. The blockchain may be present without being managed by a centralized agency or server, but may be present. Data of the blockchain is jointly collected and constituted by blocks. The blocks are connected to each other, and protected by using encryption. The blockchain fundamentally perfectly complements massive IoT through enhanced interoperability security, personal information protection, stability, and scalability. Therefore, the blockchain technology provides various functions such as inter-device interoperability, massive data traceability, autonomous interactions of different IoT systems, and massive connection stability of the 6G communication system.

3D Networking

The 6G system supports user communication of vertical scalability by integrating ground and aerial networks. A 3D BS will be provided through a low-orbit satellite and a UAV. When a new dimension is added in terms of an altitude and a related degree of freedom, a 3D connection is quire different from the existing 2D network.

Quantum Communication

In the context of the 6G network, unsupervised reinforcement learning of the network is promising. The supervised learning scheme may designate a label in a vast amount of data generated in the 6G. The labeling is not required in the unsupervised learning. Therefore, the technology may be used for autonomously constructing a complicated network expression. When the reinforcement learning and the unsupervised learning are combined, the network may be operated by a true autonomous scheme.

Unmanned Aerial Vehicle (UAV)

The unmanned aerial vehicle (UAV) or a drone will become an important element in the 6G wireless communication. In most cases, a high-speed data wireless connection is provided by using a UAV technology. A BS entity is installed in the UAV in order to provide the cellular connection. The UAV has a specific function which may not be seen in a fixed BS infrastructure, such as easy deployment, strong visible-line link, the degree of freedom in which mobility is controlled. During emergency situations such as natural disaster, the placement of a ground communication infrastructure is not enabled to be economically realized, and sometimes may not provide services in volatile environments. The UAV may easily handle this situation. The UAV will become a new paradigm of a wireless communication field. This technology facilitates three basic requirements of the wireless network, i.e., eMBB, URLLC, and mMTC/ The UAV may also support various purposes such as network connectivity enhancement, fire sensing, disaster emergency services, securing and monitoring, pollution monitoring, parking monitoring, accident monitoring, etc. Therefore, the UAV technology is recognized as one of the most important technologies for the 6G communication.

Cell-free Communication

The close integration of multiple frequencies and heterogeneous communication technologies is very important in the 6G system. As a result, the user may move smoothly from the network to another network without the need of creating any manual configuration in the device. A best network is automatically selected in an available communication technology. This will break the limitation of a cell concept in the wireless communication. Currently, user movement from one cell to another cell causes too many handovers in the network, and causes handover failures, handover latency, data loss, and pingpong effects. 6G cell-free communication will overcome all of the problems, and provide a better QoS. The cell-free communication will be achieved through different heterogeneous radios of multi-connectivity and multi-tier hybrid technologies and devices.

Wireless Information and Energy Transmission Integration

WIET uses the same field and wave as the wireless communication system. In particular, the sensor and the smartphone will be charged by using wireless power transmission during communication. The WIET is a promising technology for extending the life-span of a battery charging wireless system. Therefore, a device without the battery will be supported in the 6G communication.

Integration of Sensing and Communication

An autonomous wireless network is a function to continuously an environmental state which is dynamically changed, and exchange information between different nodes. In the 6G, the sensing will be closely integrated with the communication in order to an autonomous system.

Integration of Access Backhaul Network

In the 6G, the density of the access network will be enormous. Each access network is connected by the backhaul network such as the optical fiber and the FSO network. In order to cope with a very larger number of access networks, there will be a close integration between the access and the backhaul network.

Hologram and Beamforming

The beamforming is a signal processing procedure of adjusting an antenna array in order to transmit a radio signal. The beamforming is a sub-set of a smart antenna or advanced antenna system. The beamforming technology has several advantages such as a high call-to-noise ratio, interference prevention and denial, and high network efficiency. The hologram and beamforming (HBF) is a new beamforming method which is significantly different from the MIMO system because a software-defined antenna is used. The HBF will be a very effective approach scheme for efficient and flexible transmission and reception of the signal in a multi-antenna communication device.

Big Data Analysis

The big data analysis is a complicated process for analyzing various large-scale data sets or big data. This process guarantees perfect data management by finding hidden data, and information such as a correlation and a customer tendency which may not be known. The big data is collected from various sources such as a video, a social network, an image, and the sensor. This technology is widely used to processing vast data in the 6G system.

Large Intelligent Surface (LIS)

A THz band signal has a strong straightness, so there may be a lot of shade regions due to obstacles, and an LIS technology will be important in which the LIS is installed near such a shade region to expand a communication zone and to enable communication stability strengthening and additional services. The LIS is an artificial surface made of electromagnetic materials, and may change propagations of incoming radio waves and outgoing radio waves. The LIS may be shown as an extension of massive MIMO, but is different from the massive MIMO in terms of an array structure and an operating mechanism. Further, the LIS has an advantage of maintaining low power consumption in that the LIS operates as a reconfigurable reflector having passive element, i.e., reflects the signal only passively without using an active RF chain. Further, since each passive reflector of the LIS should independently control a phase shift of an incident signal, the reflector may be advantage for the wireless communication channel. By appropriately controlling the phase shift through an LIS controller, a reflected signal may be gathered in a target receiver in order to boost a received signal power.

The 6G communication technology described above may be applied in combination with the methods proposed in the present disclosure to be described below, or may be supplemented to specify or clarify the technical features of the methods proposed in the present disclosure. Meanwhile, the communication service proposed in the present disclosure may also be applied in combination with the 3G, 4G, and/or 5G communication technologies as well as the 6G communication technology described above.

Meanwhile, as the frequency of a high frequency band (above 6 GHz, A6G) is used in a low frequency band (below 6 GHz, B6G), it is expected that a plurality of antennas is used due to the decrease of wavelength and the increase of signal attenuation, but when a plurality of antennas is used, great overhead may occur for channel estimation and beam training.

The overhead may be reduced in case that the communication systems of a low frequency band and a high frequency band coexist. If channels are configured with similar paths in the low frequency band and the high frequency band, the spatial information of the high frequency band may be estimated by using the spatial information of the low frequency band.

A user equipment (UE) may perform the channel estimation or the beam training for the high frequency band by using a signal of the low frequency band having smaller number of antennas, and reduce the overhead.

A base station (BS) receives information for an antenna array of the UE and transmits the similarity between channel space information of the low frequency band and the high frequency band periodically. The UE that receives the information performs operations such as the channel estimation or the beam training selectively based on the similarity.

Hereinafter, it will be described the operation of obtaining spatial information of channels between the low frequency band and the high frequency band and the channel estimation and the beam training operations based on the spatial information described above in detail.

The UE and the BS need to support the communication system of the low frequency band and the high frequency band.

Federated Learning

FIG. 12 is a diagram schematically illustrating a federated learning process according to an embodiment of the present disclosure.

First, the federated learning is briefly described as below.

The federated learning (FL) is one of the distributed machine learning techniques. In the federated learning, several devices (for example, UE), which are subjects of learning, share a weight value of a local model or a parameter of gradient with a server.

Here, the server (for example, BS) collects a local model parameter of each of the UEs (1230) and updates a global parameter (integrated parameter) (1240).

In the process described above, since low data of each UE are not shared, the communication overhead may be reduced in a process of data transmission, and the personal information of users that own the UEs may be protected.

Specifically, the federated learning based on the conventional orthogonal multiple access (OFDMA) operates as shown in FIG. 12A and FIG. 12B. As shown in FIG. 12A, each of the UEs 1210 may transmit a local parameter to the BS 1220 in its own allocated resource.

Here, the BS may perform offline integration for the local parameter received from the UE. Here, the BS may calculate an average value for all local parameters, obtain a global parameter (integrated parameter) based on a plurality of local parameters, and transmit the obtained global parameter to each UE.

However, when using limited radio resources, as the number of UEs that participate in the learning increases, a longer time requires to update the global parameter.

To solve the problem described above, a research for the AirComp based federated learning has been progressed. As shown in FIG. 12B, AirComp is the scheme in which all UEs 1250 transmit the local parameters to the BS 1260 by utilizing the same resource. Here, the BS may obtain the summation of the local parameters of the received signal based on the superposition property of the analogue waveform (1270). Here, according to the AirComp based federated learning, the local parameter is transmitted through the same resource, the speed is not influenced by the number of UEs that participate in the learning.

In the present disclosure, the UE may forward the learned information to the BS with its own maximum instantaneous power (or a power adjusted to have the same reception power by the UEs in a group considering a channel change) without adjusting a transmission rate. Here, a power consumption condition may not be considered for learning low delay. Here, a range of a signal size to be transmitted by each UE is limited. When the UE transmit the gradient that the UE learned and obtained to the BS in an AirComp form, a positive gradient uses a positive signal size range, and a negative gradient uses a negative signal size range.

In the present disclosure, both the positive gradient and the negative gradient may utilize the entire signal size range by utilizing a complex number domain within a given signal size range. Accordingly, the receiving end reliability may be improved with the same transmission power, more UEs may participate in learning for a target reliability, and a learning delay time may be improved.

Furthermore, the present disclosure describes a data transmission and reception method based on a modified gradient sequence in which the entire signal size range may be utilized without regard to signs of initially received gradients by modifying the gradient sequence initially received by a BS.

Definition

The basic characters represent scalar numbers. The bolded subscript and superscript represent a vector and a matrix, respectively. The calligraph letter means a set. For example, x, x, X and

signify a scalar, a vector, a matrix, and a set, respectively. x[i] means an i^(th) entry of vector x ([x[i]]_(i=m) ^(n)=[x[m], x[m+1], . . . ,x[n]]). |x| and |x| means an absolute value of x and a cardinality of x. Lastly, real(x) and imag(x) mean a real part and an imaginary part of x.

Embodiment(s)

FIG. 13 is a flowchart illustrating a federated learning method of a BS in a wireless communication system according to an embodiment of the present disclosure.

As shown in FIG. 13 , a federated learning method (step S100) of a BS in a wireless communication system according to an embodiment of the present disclosure may includes steps S110, S130, and S150, and the detailed description is as below.

First, the BS may receive a plurality of local parameters from a plurality of UEs (step S110).

Subsequently, the BS may obtain an integrated parameter based on the plurality of local parameters (step S130).

Next, the BS may transmit the integrated parameter to the plurality of UEs (step S150). Here, the BS may remove at least one bias included in the plurality of local parameters by using the plurality of local parameters.

Here, the at least one bias may be determined based on a sign of a gradient of the local parameter in which each of the at least one bias is included. Here, a processor of the BS may determine a sign value of each of a plurality of gradients included in the plurality of local parameters by using at least one removal parameter included in the plurality of local parameters and remove the at least one bias based on a sign value of each of the plurality of gradients.

In addition, the processor may determine the sign value of each of the plurality of gradients based on the number of at least one removal parameters.

Furthermore, the processor may determine the number of at least one removal parameters based on the number of local parameters having a positive value among the plurality of local parameters.

In addition, the at least one parameter may be determined based on at least one of the number of the plurality of UEs and a transmission power of the plurality of UEs.

That is, the present disclosure proposes a method of transmission and reception by modifying the gradient sequence learned in each UE to a complex number gradient sequence when the AirComp based federated learning is performed in a situation where a signal size range transmittable by each transmission end (for example, UE) is limited. Before describing the proposed method of transmission and reception, it is assumed that the UEs participating in the learning based on CSI information are grouped, and the signals transmitted by the UEs belonged to the same group have similar reception power sensitivity.

In addition, it is assumed that the gradient values treated in the present disclosure have real number values and transmitted of which size is modified in a size between [−p, p]. Here, the modification step size may be dependent on the bit number that represents the gradient value, and the same distance mapping may be considered. Here, the method of transmission and reception of the UEs in each group is described. The following two cases are considered to utilize the gradient sequence transmitted from each transmission end in each signal size range efficiently.

Consideration 1: If all gradients use the entire signal sizes [−p, p] without regard to the sign, the transmission end is easy to treat the range of a signal size to be transmitted from each domain by the transmission end.

Consideration 2: In order to use the entire signal sizes without regard to the sign of the gradient, it is required to use a bias properly. However, since the bias is applied in a reverse direction with each other depending on the sign of the gradient, this needs to be handled properly.

The gradient sign alignment technique of a complex number domain using a bias proposed in the present disclosure has the following scheme. A biased complex number gradient sign alignment sequence is generated by applying the gradient sign alignment technique of a complex number domain using a bias and the bias insertion and ambiguity eliminating parameter insertion techniques by using an initial gradient sequence. When the modified gradient sequence having a length N of the k^(th) UE in the t^(th) iteration is ∇_(k) ^((t)), BCGSA sequence ∇ _(k) ^((t)) may be modified to the form as represented in Equation 1 below by the BS/UE.

$\begin{matrix} {{{{\overset{\_}{\nabla}}_{k}^{(t)} = \left\lbrack {{\overset{\_}{\nabla}}_{k}^{(t)}\lbrack n\rbrack} \right\rbrack_{n = 1}^{N}}{where}}{{{\overset{\_}{\nabla}}_{k}^{(t)}\lbrack n\rbrack} = \left\{ {\begin{matrix} {\frac{\left( {{\nabla_{k}^{(t)}\lbrack n\rbrack} - b} \right) + {j\alpha}}{\gamma},} & {{{{if}{\nabla_{k}^{(t)}\lbrack n\rbrack}} \geq 0},} \\ {\frac{b + {\nabla_{k}^{(t)}\lbrack n\rbrack}}{\gamma},} & {else} \end{matrix}.} \right.}} & \left\lbrack {{Equation}1} \right\rbrack \end{matrix}$

Here, ∇_(k) ^((t))[n] is the nth entry of ∇_(k) ^((t)), and “b” is a bias value. If the statistical property of an absolute value of the learned gradient is known, the bias value b is a preset value based on this. That is, ∇_(k) ^((t))[n] has a single distributed value at [0, ρ], the bias value is determined to be b=ρ/2. In addition, α may be an ambiguity eliminating parameter (or ambiguity eliminator).

$\gamma = \frac{\rho}{\sqrt{\left( {\rho - b} \right)^{2} + \alpha^{2}}}$

is a scaling factor. α may also be preconfigured as a value determined as a function of the number of participating UEs and the transmission power P, or made to a table and shared between the BS and the UE in advance.

Here, in the case that the entire signal size range ([−ρ,ρ]) is used without regard to the sign of the initial gradient included in the local parameter received from each UE, ambiguity may occur inevitably. Therefore, it is required to adjust the ambiguity. Hereinafter, the ambiguity is described: in order to utilize the signal size range efficiently, a bias application is required for positive gradient values to a negative number direction, and a bias application is required for negative gradient values to a positive number direction. That is, depending on a gradient sign, the bias is applied in a reverse direction. Due to this reason, in the case that the bias is applied based on a gradient sign for each element, the following problem occurs. Since the reception end receives a simply integrated signal, it is unable to infer how many UEs transmit a positive gradient and a negative gradient from the received integrated signal, and accordingly, eliminating the remaining bias is impossible, and therefore, the ambiguity occurs.

Referring to Equation 1 again, when the initial gradient sequence is modified, the gradient is mapped to the entire signal size range by utilizing the bias, and information may be transferred, and the imaginary part may be used for an ambiguity elimination parameter. In more detailed description, the ambiguity elimination parameter may become an indicator for counting the number of UEs for transferring the positive gradient for each element. If the number of UEs that transfers the positive gradient for each element is known, the remaining bias may be eliminated. If W₁ positive gradients and W₂(=W−W₁) negative gradients are integrated in the n^(th) reception element r^((t))[n] of the t^(th) iteration in the environment not influenced by a channel and a noise, this may be represented as Equation 2 below.

$\begin{matrix} {{\nabla_{k}^{(t)}\lbrack n\rbrack} = {\underset{{desired}{signal}}{\underset{︸}{{\sum}_{k = 1}^{W}{\nabla_{k}^{(t)}\lbrack n\rbrack}}} + \underset{{residual}{bias}}{\underset{︸}{\left( {W_{2} - W_{1}} \right)b}} + \underset{\begin{matrix} {ambiguity} \\ {eliminator} \end{matrix}}{\underset{︸}{jW_{1}\alpha}}}} & \left\lbrack {{Equation}2} \right\rbrack \end{matrix}$

Here, if the BS knows the entire number of UEs W and the value of α, the BS may determine W₁. As the number of UEs increases, the determination performance becomes abruptly increasing.

When the UEs obtains the channel information h_(k) ^((t)) in advance, adjust the phase, and transmit the information with the transmission power P in the AirComp form, the BS obtains the signal r^((t)) as represented in Equation 3 below.

r ^((t))=Σ_(k=1) ^(W) |h _(k) ^((t)) |√{square root over (P∇)} _(k) ^((t)) +n  [Equation 3]

Here, n˜CN(0,1) means a complex number additional white Gaussian noise. The BS may perform a size scaling for the received BCGSA sequence and modify to r ^((t))=γr^((t))/√{square root over (P)}Σ_(k=1) ^(W)|h_(k) ^((t))|).

Furthermore, the BS may obtain the integrated gradient sequence ∇^((t)) the corresponding UE group through the post-processing (or pre-processing) as represented in Equation 3 below.

$\begin{matrix} {{{\nabla^{(t)} = {{real}\left( {\overset{¯}{r}}^{(t)} \right)}} + {\underset{(a)}{\underset{︸}{b\left( {{2\varepsilon} - 1} \right)}}{where}\varepsilon}} = \frac{{imag}\left( {\overset{¯}{\Gamma}}^{(t)} \right)}{\alpha}} & \left\lbrack {{Equation}4} \right\rbrack \end{matrix}$

Herein, ε may mean a ratio of UEs that transmit the positive gradient value among all UEs for each element. (a) is a remaining bias compensation factor and obtained from

$\frac{W_{1} - W_{2}}{W} = {\frac{W_{1} - \left( {W - W_{1}} \right)}{W} = \frac{{2W_{1}} - W}{W}}$

FIG. 14 is a diagram illustrating a change of gradient value according to an embodiment of the present disclosure.

As shown in FIG. 14 , according to the present disclosure, for eliminating ambiguity, a constant value may be used in some real parts, and transmission and reception power may be decreased. However, without regard to a sign of each of the gradient values 1401, 1402, 1403, 1404, 1411, 1412, 1413, 1414, and 1415, the entire signal size range may be widely used (both the negative value and the positive value (1430) may be utilized). Accordingly, consequently, there is an effect of increasing a distance of each component symbol of the modified gradient sequence, and the reliability of the reception end may be increased while almost the same average power is consumed in comparison with the conventional art.

The present disclosure specifies a method of utilizing single bias based I/Q 2 channel, but the method may be extended to a method of utilizing 2K channel (I/Q channel+time-frequency space) by utilizing a plurality of biases of K.

The present disclosure describes a transmission and reception method by assuming a single UE group, but the method may be identically applied for a plurality of UE groups (distinguished for each different CSI). For example, through the integration process weighted by a UE number ratio of each group in comparison with the number of UEs that participate in the entire learning for the integrated gradients obtained each group, the global gradient (integrated gradient) may be obtained. For example, when the number of UEs that participate in the entire learning and the number of UEs that participate in the learning of the i^(th) group are denoted by W=(Σ_(i=1) ^(G)W_(i)) and W_(i), respectively, and the integrated gradient sequence is denoted by ∇_(i) ^((t)), the integrated gradient sequence ∇^((t)) may be obtained in the form as represented in Equation 5 below.

$\begin{matrix} {{\nabla^{(t)} = {\Sigma}_{i = 1}^{G}}\frac{W_{i}}{W}\nabla_{i}^{(t)}} & \left\lbrack {{Equation}5} \right\rbrack \end{matrix}$

FIGS. 15 and 16 are graphs illustrating the technical effect of the federated learning method according to an embodiment of the present disclosure.

As shown in FIG. 15 , an MSE may be changed according to transmission/reception techniques according to an embodiment of the present disclosure.

Table 3 represents a transmission power provided for each UE according to an embodiment of the present disclosure.

TABLE 3 Average transmission power comparison P 10 dB 20 dB Conv 3.36 33 Conv[rep] 6.72 67 BCSGA 5.6 56

That is, it is assumed that the gradients obtained by each of the UEs are i.i.d. and have a single distributed value between [−1, 1], channels have the same size in each UE and BS, and the UE compensates the phase through precoding and transmits it to the BS. The mean square errors (MSEs) are compared between the conventional scheme (conv) in which all UEs participate in the learning and perform a simple power control and the scheme in which a power control is performed by utilizing the proposed BCGSA.

According to FIG. 15 and FIG. 16 , it is identified that the MSC performance is improved for all cases including the case in which a transmission power is 10 dB and the case in which a transmission power is 10 Db. The proposed scheme is better than the conventional scheme according to a transmission power level. In the environment in which a transmission power level is 20 dB, the proposed scheme has better MSE performance than the conventional scheme.

In the case that it is hard to increase a transmission power sufficiently, the operation range may be changed to a high SNR regime through an array gain by utilizing multiple antennas.

Devices Used in Wireless Communication Systems

Although not limited thereto, various proposals of the present disclosure described above can be applied to various fields requiring wireless communication/connection (eg, 6G) between devices.

Hereinafter, it will be more specifically illustrated with reference to the drawings. In the following drawings/description, the same reference numerals may represent the same or corresponding hardware blocks, software blocks or functional blocks unless otherwise specified.

FIG. 17 illustrates a communication system 1 applied to the present disclosure.

Referring to FIG. 17 , a communication system 1 applied to the present disclosure includes wireless devices, Base Stations (BSs), and a network. Herein, the wireless devices represent devices performing communication using Radio Access Technology (RAT) (e.g., 5G New RAT (NR)) or Long-Term Evolution (LTE)) and may be referred to as communication/radio/5G devices. The wireless devices may include, without being limited to, a robot 100 a, vehicles 100 b-1 and 100 b-2, an eXtended Reality (XR) device 100 c, a hand-held device 100 d, a home appliance 100 e, an Internet of Things (IoT) device 100 f, and an Artificial Intelligence (AI) device/server 400. For example, the vehicles may include a vehicle having a wireless communication function, an autonomous driving vehicle, and a vehicle capable of performing communication between vehicles. Herein, the vehicles may include an Unmanned Aerial Vehicle (UAV) (e.g., a drone). The XR device may include an Augmented Reality (AR)/Virtual Reality (VR)/Mixed Reality (MR) device and may be implemented in the form of a Head-Mounted Device (HMD), a Head-Up Display (HUD) mounted in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance device, a digital signage, a vehicle, a robot, etc. The hand-held device may include a smartphone, a smartpad, a wearable device (e.g., a smartwatch or a smartglasses), and a computer (e.g., a notebook). The home appliance may include a TV, a refrigerator, and a washing machine. The IoT device may include a sensor and a smartmeter. For example, the BSs and the network may be implemented as wireless devices and a specific wireless device 200 a may operate as a BS/network node with respect to other wireless devices.

The wireless devices 100 a to 100 f may be connected to the network 300 via the BSs 200. An AI technology may be applied to the wireless devices 100 a to 100 f and the wireless devices 100 a to 100 f may be connected to the AI server 400 via the network 300. The network 300 may be configured using a 3G network, a 4G (e.g., LTE) network, or a 5G (e.g., NR) network. Although the wireless devices 100 a to 100 f may communicate with each other through the BSs 200/network 300, the wireless devices 100 a to 100 f may perform direct communication (e.g., sidelink communication) with each other without passing through the BSs/network. For example, the vehicles 100 b-1 and 100 b-2 may perform direct communication (e.g. Vehicle-to-Vehicle (V2V)/Vehicle-to-everything (V2X) communication). The IoT device (e.g., a sensor) may perform direct communication with other IoT devices (e.g., sensors) or other wireless devices 100 a to 100 f.

Wireless communication/connection 150 a, 150 b may be performed between the wireless devices 100 a to 100 f/base station 200-base station 200/wireless devices 100 a to 100 f. Here, wireless communication/connection may be performed through various radio access technologies (eg, 5G NR) for uplink/downlink communication 150 a and sidelink communication 150 b (or D2D communication). Through the wireless communication/connection 150 a and 150 b, the wireless device and the base station/wireless device may transmit/receive radio signals to each other. For example, the wireless communication/connection 150 a and 150 b may transmit/receive signals through various physical channels based on all/partial processes of FIG. To this end, based on the various proposals of the present disclosure, various configuration information setting processes for transmitting/receiving radio signals, various signal processing processes (eg, channel encoding/decoding, modulation/demodulation, resource mapping/demapping, etc.) At least a part of a resource allocation process may be performed.

FIG. 18 illustrates wireless devices applicable to the present disclosure.

Referring to FIG. 18 , a first wireless device 100 and a second wireless device 200 may transmit radio signals through a variety of RATs (e.g., LTE and NR). Herein, {the first wireless device 100 and the second wireless device 200} may correspond to {the wireless device 100 x and the BS 200} and/or {the wireless device 100 x and the wireless device 100 x} of FIG. 17 .

The first wireless device 100 may include one or more processors 102 and one or more memories 104 and additionally further include one or more transceivers 106 and/or one or more antennas 108. The processor(s) 102 may control the memory(s) 104 and/or the transceiver(s) 106 and may be configured to implement the descriptions, functions, procedures, proposals, methods, and/or operational flowcharts disclosed in this document. For example, the processor(s) 102 may process information within the memory(s) 104 to generate first information/signals and then transmit radio signals including the first information/signals through the transceiver(s) 106. The processor(s) 102 may receive radio signals including second information/signals through the transceiver 106 and then store information obtained by processing the second information/signals in the memory(s) 104. The memory(s) 104 may be connected to the processor(s) 102 and may store a variety of information related to operations of the processor(s) 102. For example, the memory(s) 104 may store software code including commands for performing a part or the entirety of processes controlled by the processor(s) 102 or for performing the descriptions, functions, procedures, proposals, methods, and/or operational flowcharts disclosed in this document. Herein, the processor(s) 102 and the memory(s) 104 may be a part of a communication modem/circuit/chip designed to implement RAT (e.g., LTE or NR). The transceiver(s) 106 may be connected to the processor(s) 102 and transmit and/or receive radio signals through one or more antennas 108. Each of the transceiver(s) 106 may include a transmitter and/or a receiver. The transceiver(s) 106 may be interchangeably used with Radio Frequency (RF) unit(s). In the present disclosure, the wireless device may represent a communication modem/circuit/chip.

The second wireless device 200 may include one or more processors 202 and one or more memories 204 and additionally further include one or more transceivers 206 and/or one or more antennas 208. The processor(s) 202 may control the memory(s) 204 and/or the transceiver(s) 206 and may be configured to implement the descriptions, functions, procedures, proposals, methods, and/or operational flowcharts disclosed in this document. For example, the processor(s) 202 may process information within the memory(s) 204 to generate third information/signals and then transmit radio signals including the third information/signals through the transceiver(s) 206. The processor(s) 202 may receive radio signals including fourth information/signals through the transceiver(s) 106 and then store information obtained by processing the fourth information/signals in the memory(s) 204. The memory(s) 204 may be connected to the processor(s) 202 and may store a variety of information related to operations of the processor(s) 202. For example, the memory(s) 204 may store software code including commands for performing a part or the entirety of processes controlled by the processor(s) 202 or for performing the descriptions, functions, procedures, proposals, methods, and/or operational flowcharts disclosed in this document. Herein, the processor(s) 202 and the memory(s) 204 may be a part of a communication modem/circuit/chip designed to implement RAT (e.g., LTE or NR). The transceiver(s) 206 may be connected to the processor(s) 202 and transmit and/or receive radio signals through one or more antennas 208. Each of the transceiver(s) 206 may include a transmitter and/or a receiver. The transceiver(s) 206 may be interchangeably used with RF unit(s). In the present disclosure, the wireless device may represent a communication modem/circuit/chip.

Hereinafter, hardware elements of the wireless devices 100 and 200 will be described more specifically. One or more protocol layers may be implemented by, without being limited to, one or more processors 102 and 202. For example, the one or more processors 102 and 202 may implement one or more layers (e.g., functional layers such as PHY, MAC, RLC, PDCP, RRC, and SDAP). The one or more processors 102 and 202 may generate one or more Protocol Data Units (PDUs) and/or one or more Service Data Unit (SDUs) according to the descriptions, functions, procedures, proposals, methods, and/or operational flowcharts disclosed in this document. The one or more processors 102 and 202 may generate messages, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and/or operational flowcharts disclosed in this document. The one or more processors 102 and 202 may generate signals (e.g., baseband signals) including PDUs, SDUs, messages, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and/or operational flowcharts disclosed in this document and provide the generated signals to the one or more transceivers 106 and 206. The one or more processors 102 and 202 may receive the signals (e.g., baseband signals) from the one or more transceivers 106 and 206 and acquire the PDUs, SDUs, messages, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and/or operational flowcharts disclosed in this document.

The one or more processors 102 and 202 may be referred to as controllers, microcontrollers, microprocessors, or microcomputers. The one or more processors 102 and 202 may be implemented by hardware, firmware, software, or a combination thereof. As an example, one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), one or more Digital Signal Processing Devices (DSPDs), one or more Programmable Logic Devices (PLDs), or one or more Field Programmable Gate Arrays (FPGAs) may be included in the one or more processors 102 and 202. The descriptions, functions, procedures, proposals, methods, and/or operational flowcharts disclosed in this document may be implemented using firmware or software and the firmware or software may be configured to include the modules, procedures, or functions. Firmware or software configured to perform the descriptions, functions, procedures, proposals, methods, and/or operational flowcharts disclosed in this document may be included in the one or more processors 102 and 202 or stored in the one or more memories 104 and 204 so as to be driven by the one or more processors 102 and 202. The descriptions, functions, procedures, proposals, methods, and/or operational flowcharts disclosed in this document may be implemented using firmware or software in the form of code, commands, and/or a set of commands.

The one or more memories 104 and 204 may be connected to the one or more processors 102 and 202 and store various types of data, signals, messages, information, programs, code, instructions, and/or commands. The one or more memories 104 and 204 may be configured by Read-Only Memories (ROMs), Random Access Memories (RAMs), Electrically Erasable Programmable Read-Only Memories (EPROMs), flash memories, hard drives, registers, cash memories, computer-readable storage media, and/or combinations thereof. The one or more memories 104 and 204 may be located at the interior and/or exterior of the one or more processors 102 and 202. The one or more memories 104 and 204 may be connected to the one or more processors 102 and 202 through various technologies such as wired or wireless connection.

The one or more transceivers 106 and 206 may transmit user data, control information, and/or radio signals/channels, mentioned in the methods and/or operational flowcharts of this document, to one or more other devices. The one or more transceivers 106 and 206 may receive user data, control information, and/or radio signals/channels, mentioned in the descriptions, functions, procedures, proposals, methods, and/or operational flowcharts disclosed in this document, from one or more other devices. For example, the one or more transceivers 106 and 206 may be connected to the one or more processors 102 and 202 and transmit and receive radio signals. For example, the one or more processors 102 and 202 may perform control so that the one or more transceivers 106 and 206 may transmit user data, control information, or radio signals to one or more other devices. The one or more processors 102 and 202 may perform control so that the one or more transceivers 106 and 206 may receive user data, control information, or radio signals from one or more other devices. The one or more transceivers 106 and 206 may be connected to the one or more antennas 108 and 208 and the one or more transceivers 106 and 206 may be configured to transmit and receive user data, control information, and/or radio signals/channels, mentioned in the descriptions, functions, procedures, proposals, methods, and/or operational flowcharts disclosed in this document, through the one or more antennas 108 and 208. In this document, the one or more antennas may be a plurality of physical antennas or a plurality of logical antennas (e.g., antenna ports). The one or more transceivers 106 and 206 may convert received radio signals/channels etc. from RF band signals into baseband signals in order to process received user data, control information, radio signals/channels, etc. using the one or more processors 102 and 202. The one or more transceivers 106 and 206 may convert the user data, control information, radio signals/channels, etc. processed using the one or more processors 102 and 202 from the base band signals into the RF band signals. To this end, the one or more transceivers 106 and 206 may include (analog) oscillators and/or filters.

FIG. 19 illustrates a signal process circuit for a transmission signal.

Referring to FIG. 19 , a signal processing circuit 1000 may include scramblers 1010, modulators 1020, a layer mapper 1030, a precoder 1040, resource mappers 1050, and signal generators 1060. An operation/function of FIG. 19 may be performed, without being limited to, the processors 102 and 202 and/or the transceivers 106 and 206 of FIG. 18 . Hardware elements of FIG. 19 may be implemented by the processors 102 and 202 and/or the transceivers 106 and 206 of FIG. 18 . For example, blocks 1010 to 1060 may be implemented by the processors 102 and 202 of FIG. 18 . Alternatively, the blocks 1010 to 1050 may be implemented by the processors 102 and 202 of FIG. 18 and the block 1060 may be implemented by the transceivers 106 and 206 of FIG. 18 .

Codewords may be converted into radio signals via the signal processing circuit 1000 of FIG. 19 . Herein, the codewords are encoded bit sequences of information blocks. The information blocks may include transport blocks (e.g., a UL-SCH transport block, a DL-SCH transport block). The radio signals may be transmitted through various physical channels (e.g., a PUSCH and a PDSCH).

Specifically, the codewords may be converted into scrambled bit sequences by the scramblers 1010. Scramble sequences used for scrambling may be generated based on an initialization value, and the initialization value may include ID information of a wireless device. The scrambled bit sequences may be modulated to modulation symbol sequences by the modulators 1020. A modulation scheme may include pi/2-Binary Phase Shift Keying (pi/2-BPSK), m-Phase Shift Keying (m-PSK), and m-Quadrature Amplitude Modulation (m-QAM). Complex modulation symbol sequences may be mapped to one or more transport layers by the layer mapper 1030. Modulation symbols of each transport layer may be mapped (precoded) to corresponding antenna port(s) by the precoder 1040. Outputs z of the precoder 1040 may be obtained by multiplying outputs y of the layer mapper 1030 by an N*M precoding matrix W. Herein, N is the number of antenna ports and M is the number of transport layers. The precoder 1040 may perform precoding after performing transform precoding (e.g., DFT) for complex modulation symbols. Alternatively, the precoder 1040 may perform precoding without performing transform precoding.

The resource mappers 1050 may map modulation symbols of each antenna port to time-frequency resources. The time-frequency resources may include a plurality of symbols (e.g., a CP-OFDMA symbols and DFT-s-OFDMA symbols) in the time domain and a plurality of subcarriers in the frequency domain. The signal generators 1060 may generate radio signals from the mapped modulation symbols and the generated radio signals may be transmitted to other devices through each antenna. For this purpose, the signal generators 1060 may include Inverse Fast Fourier Transform (IFFT) modules, Cyclic Prefix (CP) inserters, Digital-to-Analog Converters (DACs), and frequency up-converters.

Signal processing procedures for a signal received in the wireless device may be configured in a reverse manner of the signal processing procedures 1010 to 1060 of FIG. 19 . For example, the wireless devices (e.g., 100 and 200 of FIG. 18 ) may receive radio signals from the exterior through the antenna ports/transceivers. The received radio signals may be converted into baseband signals through signal restorers. To this end, the signal restorers may include frequency downlink converters, Analog-to-Digital Converters (ADCs), CP remover, and Fast Fourier Transform (FFT) modules. Next, the baseband signals may be restored to codewords through a resource demapping procedure, a postcoding procedure, a demodulation processor, and a descrambling procedure. The codewords may be restored to original information blocks through decoding. Therefore, a signal processing circuit (not illustrated) for a reception signal may include signal restorers, resource demappers, a postcoder, demodulators, descramblers, and decoders.

FIG. 20 illustrates another example of a wireless device applied to the present disclosure. The wireless device may be implemented in various forms according to a use-case/service (refer to FIG. 17 and FIGS. 21 to 26 ).

Referring to FIG. 20 , wireless devices 100 and 200 may correspond to the wireless devices 100 and 200 of FIG. 18 and may be configured by various elements, components, units/portions, and/or modules. For example, each of the wireless devices 100 and 200 may include a communication unit 110, a control unit 120, a memory unit 130, and additional components 140. The communication unit may include a communication circuit 112 and transceiver(s) 114. For example, the communication circuit 112 may include the one or more processors 102 and 202 and/or the one or more memories 104 and 204 of FIG. 18 . For example, the transceiver(s) 114 may include the one or more transceivers 106 and 206 and/or the one or more antennas 108 and 208 of FIG. 18 . The control unit 120 is electrically connected to the communication unit 110, the memory 130, and the additional components 140 and controls overall operation of the wireless devices. For example, the control unit 120 may control an electric/mechanical operation of the wireless device based on programs/code/commands/information stored in the memory unit 130. The control unit 120 may transmit the information stored in the memory unit 130 to the exterior (e.g., other communication devices) via the communication unit 110 through a wireless/wired interface or store, in the memory unit 130, information received through the wireless/wired interface from the exterior (e.g., other communication devices) via the communication unit 110.

The additional components 140 may be variously configured according to types of wireless devices. For example, the additional components 140 may include at least one of a power unit/battery, input/output (I/O) unit, a driving unit, and a computing unit. The wireless device may be implemented in the form of, without being limited to, the robot (100 a of FIG. 17 ), the vehicles (100 b-1 and 100 b-2 of FIG. 17 ), the XR device (100 c of FIG. 17 ), the hand-held device (100 d of FIG. 17 ), the home appliance (100 e of FIG. 17 ), the IoT device (100 f of FIG. 17 ), a digital broadcast terminal, a hologram device, a public safety device, an MTC device, a medicine device, a fintech device (or a finance device), a security device, a climate/environment device, the AI server/device (400 of FIG. 17 ), the BSs (200 of FIG. 17 ), a network node, etc. The wireless device may be used in a mobile or fixed place according to a use-example/service.

In FIG. 20 , the entirety of the various elements, components, units/portions, and/or modules in the wireless devices 100 and 200 may be connected to each other through a wired interface or at least a part thereof may be wirelessly connected through the communication unit 110. For example, in each of the wireless devices 100 and 200, the control unit 120 and the communication unit 110 may be connected by wire and the control unit 120 and first units (e.g., 130 and 140) may be wirelessly connected through the communication unit 110. Each element, component, unit/portion, and/or module within the wireless devices 100 and 200 may further include one or more elements. For example, the control unit 120 may be configured by a set of one or more processors. As an example, the control unit 120 may be configured by a set of a communication control processor, an application processor, an Electronic Control Unit (ECU), a graphical processing unit, and a memory control processor. As another example, the memory 130 may be configured by a Random Access Memory (RAM), a Dynamic RAM (DRAM), a Read Only Memory (ROM)), a flash memory, a volatile memory, a non-volatile memory, and/or a combination thereof.

Hereinafter, an example of implementing FIG. 20 will be described in detail with reference to the drawings.

FIG. 21 illustrates a hand-held device applied to the present disclosure. The hand-held device may include a smartphone, a smartpad, a wearable device (e.g., a smartwatch or a smartglasses), or a portable computer (e.g., a notebook). The hand-held device may be referred to as a mobile station (MS), a user terminal (UT), a Mobile Subscriber Station (MSS), a Subscriber Station (SS), an Advanced Mobile Station (AMS), or a Wireless Terminal (WT).

Referring to FIG. 21 , a hand-held device 100 may include an antenna unit 108, a communication unit 110, a control unit 120, a memory unit 130, a power supply unit 140 a, an interface unit 140 b, and an I/O unit 140 c. The antenna unit 108 may be configured as a part of the communication unit 110. Blocks 110 to 130/140 a to 140 c correspond to the blocks 110 to 130/140 of FIG. 20 , respectively.

The communication unit 110 may transmit and receive signals (e.g., data and control signals) to and from other wireless devices or BSs. The control unit 120 may perform various operations by controlling constituent elements of the hand-held device 100. The control unit 120 may include an Application Processor (AP). The memory unit 130 may store data/parameters/programs/code/commands needed to drive the hand-held device 100. The memory unit 130 may store input/output data/information. The power supply unit 140 a may supply power to the hand-held device 100 and include a wired/wireless charging circuit, a battery, etc. The interface unit 140 b may support connection of the hand-held device 100 to other external devices. The interface unit 140 b may include various ports (e.g., an audio I/O port and a video I/O port) for connection with external devices. The I/O unit 140 c may input or output video information/signals, audio information/signals, data, and/or information input by a user. The I/O unit 140 c may include a camera, a microphone, a user input unit, a display unit 140 d, a speaker, and/or a haptic module.

As an example, in the case of data communication, the I/O unit 140 c may acquire information/signals (e.g., touch, text, voice, images, or video) input by a user and the acquired information/signals may be stored in the memory unit 130. The communication unit 110 may convert the information/signals stored in the memory into radio signals and transmit the converted radio signals to other wireless devices directly or to a BS. The communication unit 110 may receive radio signals from other wireless devices or the BS and then restore the received radio signals into original information/signals. The restored information/signals may be stored in the memory unit 130 and may be output as various types (e.g., text, voice, images, video, or haptic) through the I/O unit 140 c.

FIG. 22 illustrates a vehicle or an autonomous driving vehicle applied to the present disclosure. The vehicle or autonomous driving vehicle may be implemented by a mobile robot, a car, a train, a manned/unmanned Aerial Vehicle (AV), a ship, etc.

Referring to FIG. 22 , a vehicle or autonomous driving vehicle 100 may include an antenna unit 108, a communication unit 110, a control unit 120, a driving unit 140 a, a power supply unit 140 b, a sensor unit 140 c, and an autonomous driving unit 140 d. The antenna unit 108 may be configured as a part of the communication unit 110. The blocks 110/130/140 a to 140 d correspond to the blocks 110/130/140 of FIG. 20 , respectively.

The communication unit 110 may transmit and receive signals (e.g., data and control signals) to and from external devices such as other vehicles, BSs (e.g., gNBs and road side units), and servers. The control unit 120 may perform various operations by controlling elements of the vehicle or the autonomous driving vehicle 100. The control unit 120 may include an Electronic Control Unit (ECU). The driving unit 140 a may cause the vehicle or the autonomous driving vehicle 100 to drive on a road. The driving unit 140 a may include an engine, a motor, a powertrain, a wheel, a brake, a steering device, etc. The power supply unit 140 b may supply power to the vehicle or the autonomous driving vehicle 100 and include a wired/wireless charging circuit, a battery, etc. The sensor unit 140 c may acquire a vehicle state, ambient environment information, user information, etc. The sensor unit 140 c may include an Inertial Measurement Unit (IMU) sensor, a collision sensor, a wheel sensor, a speed sensor, a slope sensor, a weight sensor, a heading sensor, a position module, a vehicle forward/backward sensor, a battery sensor, a fuel sensor, a tire sensor, a steering sensor, a temperature sensor, a humidity sensor, an ultrasonic sensor, an illumination sensor, a pedal position sensor, etc. The autonomous driving unit 140 d may implement technology for maintaining a lane on which a vehicle is driving, technology for automatically adjusting speed, such as adaptive cruise control, technology for autonomously driving along a determined path, technology for driving by automatically setting a path if a destination is set, and the like.

For example, the communication unit 110 may receive map data, traffic information data, etc. from an external server. The autonomous driving unit 140 d may generate an autonomous driving path and a driving plan from the obtained data. The control unit 120 may control the driving unit 140 a such that the vehicle or the autonomous driving vehicle 100 may move along the autonomous driving path according to the driving plan (e.g., speed/direction control). In the middle of autonomous driving, the communication unit 110 may aperiodically/periodically acquire recent traffic information data from the external server and acquire surrounding traffic information data from neighboring vehicles. In the middle of autonomous driving, the sensor unit 140 c may obtain a vehicle state and/or surrounding environment information. The autonomous driving unit 140 d may update the autonomous driving path and the driving plan based on the newly obtained data/information. The communication unit 110 may transfer information about a vehicle position, the autonomous driving path, and/or the driving plan to the external server. The external server may predict traffic information data using AI technology, etc., based on the information collected from vehicles or autonomous driving vehicles and provide the predicted traffic information data to the vehicles or the autonomous driving vehicles.

FIG. 23 illustrates a vehicle applied to the present disclosure. The vehicle may be implemented as a transport means, an aerial vehicle, a ship, etc.

Referring to FIG. 23 , a vehicle 100 may include a communication unit 110, a control unit 120, a memory unit 130, an I/O unit 140 a, and a positioning unit 140 b. Herein, the blocks 110 to 130/140 a and 140 b correspond to blocks 110 to 130/140 of FIG. 20 .

The communication unit 110 may transmit and receive signals (e.g., data and control signals) to and from external devices such as other vehicles or BSs. The control unit 120 may perform various operations by controlling constituent elements of the vehicle 100. The memory unit 130 may store data/parameters/programs/code/commands for supporting various functions of the vehicle 100. The I/O unit 140 a may output an AR/VR object based on information within the memory unit 130. The I/O unit 140 a may include an HUD. The positioning unit 140 b may acquire information about the position of the vehicle 100. The position information may include information about an absolute position of the vehicle 100, information about the position of the vehicle 100 within a traveling lane, acceleration information, and information about the position of the vehicle 100 from a neighboring vehicle. The positioning unit 140 b may include a GPS and various sensors.

As an example, the communication unit 110 of the vehicle 100 may receive map information and traffic information from an external server and store the received information in the memory unit 130. The positioning unit 140 b may obtain the vehicle position information through the GPS and various sensors and store the obtained information in the memory unit 130. The control unit 120 may generate a virtual object based on the map information, traffic information, and vehicle position information and the I/O unit 140 a may display the generated virtual object in a window in the vehicle (1410 and 1420). The control unit 120 may determine whether the vehicle 100 normally drives within a traveling lane, based on the vehicle position information. If the vehicle 100 abnormally exits from the traveling lane, the control unit 120 may display a warning on the window in the vehicle through the I/O unit 140 a. In addition, the control unit 120 may broadcast a warning message regarding driving abnormity to neighboring vehicles through the communication unit 110. According to situation, the control unit 120 may transmit the vehicle position information and the information about driving/vehicle abnormality to related organizations.

FIG. 24 illustrates an XR device applied to the present disclosure. The XR device may be implemented by an HMD, an HUD mounted in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance, a digital signage, a vehicle, a robot, etc.

Referring to FIG. 24 , an XR device 100 a may include a communication unit 110, a control unit 120, a memory unit 130, an I/O unit 140 a, a sensor unit 140 b, and a power supply unit 140 c. Herein, the blocks 110 to 130/140 a to 140 c correspond to the blocks 110 to 130/140 of FIG. 20 , respectively.

The communication unit 110 may transmit and receive signals (e.g., media data and control signals) to and from external devices such as other wireless devices, hand-held devices, or media servers. The media data may include video, images, and sound. The control unit 120 may perform various operations by controlling constituent elements of the XR device 100 a. For example, the control unit 120 may be configured to control and/or perform procedures such as video/image acquisition, (video/image) encoding, and metadata generation and processing. The memory unit 130 may store data/parameters/programs/code/commands needed to drive the XR device 100 a/generate XR object. The I/O unit 140 a may obtain control information and data from the exterior and output the generated XR object. The I/O unit 140 a may include a camera, a microphone, a user input unit, a display unit, a speaker, and/or a haptic module. The sensor unit 140 b may obtain an XR device state, surrounding environment information, user information, etc. The sensor unit 140 b may include a proximity sensor, an illumination sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, a light sensor, a microphone and/or a radar. The power supply unit 140 c may supply power to the XR device 100 a and include a wired/wireless charging circuit, a battery, etc.

For example, the memory unit 130 of the XR device 100 a may include information (e.g., data) needed to generate the XR object (e.g., an AR/VR/MR object). The I/O unit 140 a may receive a command for manipulating the XR device 100 a from a user and the control unit 120 may drive the XR device 100 a according to a driving command of a user. For example, when a user desires to watch a film or news through the XR device 100 a, the control unit 120 transmits content request information to another device (e.g., a hand-held device 100 b) or a media server through the communication unit 130. The communication unit 130 may download/stream content such as films or news from another device (e.g., the hand-held device 100 b) or the media server to the memory unit 130. The control unit 120 may control and/or perform procedures such as video/image acquisition, (video/image) encoding, and metadata generation/processing with respect to the content and generate/output the XR object based on information about a surrounding space or a real object obtained through the I/O unit 140 a/sensor unit 140 b.

The XR device 100 a may be wirelessly connected to the hand-held device 100 b through the communication unit 110 and the operation of the XR device 100 a may be controlled by the hand-held device 100 b. For example, the hand-held device 100 b may operate as a controller of the XR device 100 a. To this end, the XR device 100 a may obtain information about a 3D position of the hand-held device 100 b and generate and output an XR object corresponding to the hand-held device 100 b.

FIG. 25 illustrates a robot applied to the present disclosure. The robot may be categorized into an industrial robot, a medical robot, a household robot, a military robot, etc., according to a used purpose or field.

Referring to FIG. 25 , a robot 100 may include a communication unit 110, a control unit 120, a memory unit 130, an I/O unit 140 a, a sensor unit 140 b, and a driving unit 140 c. Herein, the blocks 110 to 130/140 a to 140 c correspond to the blocks 110 to 130/140 of FIG. 20 , respectively.

The communication unit 110 may transmit and receive signals (e.g., driving information and control signals) to and from external devices such as other wireless devices, other robots, or control servers. The control unit 120 may perform various operations by controlling constituent elements of the robot 100. The memory unit 130 may store data/parameters/programs/code/commands for supporting various functions of the robot 100. The I/O unit 140 a may obtain information from the exterior of the robot 100 and output information to the exterior of the robot 100. The I/O unit 140 a may include a camera, a microphone, a user input unit, a display unit, a speaker, and/or a haptic module. The sensor unit 140 b may obtain internal information of the robot 100, surrounding environment information, user information, etc. The sensor unit 140 b may include a proximity sensor, an illumination sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, a light sensor, a microphone, a radar, etc. The driving unit 140 c may perform various physical operations such as movement of robot joints. In addition, the driving unit 140 c may cause the robot 100 to travel on the road or to fly. The driving unit 140 c may include an actuator, a motor, a wheel, a brake, a propeller, etc.

FIG. 26 illustrates an AI device applied to the present disclosure. The AI device may be implemented by a fixed device or a mobile device, such as a TV, a projector, a smartphone, a PC, a notebook, a digital broadcast terminal, a tablet PC, a wearable device, a Set Top Box (STB), a radio, a washing machine, a refrigerator, a digital signage, a robot, a vehicle, etc.

Referring to FIG. 26 , an AI device 100 may include a communication unit 110, a control unit 120, a memory unit 130, an I/O unit 140 a/140 b, a learning processor unit 140 c, and a sensor unit 140 d. The blocks 110 to 130/140 a to 140 d correspond to blocks 110 to 130/140 of FIG. 20 , respectively.

The communication unit 110 may transmit and receive wired/radio signals (e.g., sensor information, user input, learning models, or control signals) to and from external devices such as other AI devices (e.g., 100 x, 200, or 400 of FIG. 17 ) or an AI server (e.g., 400 of FIG. 17 ) using wired/wireless communication technology. To this end, the communication unit 110 may transmit information within the memory unit 130 to an external device and transmit a signal received from the external device to the memory unit 130.

The control unit 120 may determine at least one feasible operation of the AI device 100, based on information which is determined or generated using a data analysis algorithm or a machine learning algorithm. The control unit 120 may perform an operation determined by controlling constituent elements of the AI device 100. For example, the control unit 120 may request, search, receive, or use data of the learning processor unit 140 c or the memory unit 130 and control the constituent elements of the AI device 100 to perform a predicted operation or an operation determined to be preferred among at least one feasible operation. The control unit 120 may collect history information including the operation contents of the AI device 100 and operation feedback by a user and store the collected information in the memory unit 130 or the learning processor unit 140 c or transmit the collected information to an external device such as an AI server (400 of FIG. 17 ). The collected history information may be used to update a learning model.

The memory unit 130 may store data for supporting various functions of the AI device 100. For example, the memory unit 130 may store data obtained from the input unit 140 a, data obtained from the communication unit 110, output data of the learning processor unit 140 c, and data obtained from the sensor unit 140. The memory unit 130 may store control information and/or software code needed to operate/drive the control unit 120.

The input unit 140 a may acquire various types of data from the exterior of the AI device 100. For example, the input unit 140 a may acquire learning data for model learning, and input data to which the learning model is to be applied. The input unit 140 a may include a camera, a microphone, and/or a user input unit. The output unit 140 b may generate output related to a visual, auditory, or tactile sense. The output unit 140 b may include a display unit, a speaker, and/or a haptic module. The sensing unit 140 may obtain at least one of internal information of the AI device 100, surrounding environment information of the AI device 100, and user information, using various sensors. The sensor unit 140 may include a proximity sensor, an illumination sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, a light sensor, a microphone, and/or a radar.

The learning processor unit 140 c may learn a model consisting of artificial neural networks, using learning data. The learning processor unit 140 c may perform AI processing together with the learning processor unit of the AI server (400 of FIG. 17 ).

The learning processor unit 140 c may process information received from an external device through the communication unit 110 and/or information stored in the memory unit 130. In addition, an output value of the learning processor unit 140 c may be transmitted to the external device through the communication unit 110 and may be stored in the memory unit 130.

Here, the wireless communication technology implemented in the wireless device (100, 200) of the present disclosure may include LTE, NR, and 6G as well as Narrowband Internet of Things for low power communication. At this time, for example, NB-IoT technology may be an example of LPWAN (Low Power Wide Area Network) technology, and may be implemented in standards such as LTE Cat NB1 and/or LTE Cat NB2. not. Additionally or alternatively, the wireless communication technology implemented in the wireless device (100, 200) of the present disclosure may perform communication based on LTE-M technology. At this time, as an example, LTE-M technology may be an example of LPWAN technology, and may be called various names such as eMTC (enhanced machine type communication). For example, LTE-M technologies are 1) LTE CAT 0, 2) LTE Cat M1, 3) LTE Cat M2, 4) LTE non-BL (non-Bandwidth Limited), 5) LTE-MTC, 6) LTE Machine Type Communication, and/or 7) It may be implemented in at least one of various standards such as LTE M, and is not limited to the above-mentioned names. Additionally or alternatively, the wireless communication technology implemented in the wireless device (100, 200) of the present disclosure is at least one of ZigBee, Bluetooth, and Low Power Wide Area Network (LPWAN) considering low power communication It may include any one, and is not limited to the above-mentioned names. For example, ZigBee technology can generate personal area networks (PANs) related to small/low-power digital communication based on various standards such as IEEE 802.15.4, and can be called various names.

The above disclosure can be implemented as computer readable code on a medium on which a program is recorded. The computer-readable medium includes all types of recording devices in which data that can be read by a computer system is stored. Examples of computer-readable media include Hard Disk Drive (HDD), Solid State Disk (SSD), Silicon Disk Drive (SDD), ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc., and also includes those implemented in the form of a carrier wave (eg, transmission over the Internet). Accordingly, the above detailed description should not be construed as limiting in all respects and should be considered illustrative. The scope of this disclosure should be determined by reasonable interpretation of the appended claims, and all changes within the equivalent scope of this disclosure are included in the scope of this disclosure. 

1. A method of performing a federated learning in a wireless communication system, the method performed by a base station comprising: performing, with a plurality of terminals, a random access procedure for performing the federated learning; wherein the random access procedure comprises: receiving, from the plurality of terminals, a random access preamble; transmitting, to the plurality of terminals, a random access response; receiving, from the plurality of terminals, a connection request message from based on the random access response; and transmitting, to the plurality of terminals, a contention resolution message; receiving, from the plurality of terminals, a plurality of local parameters; obtaining an integrated parameter based on the plurality of local parameters; and transmitting, to the plurality of terminals, the integrated parameter, wherein obtaining the integrated parameter includes: removing at least one bias included in the plurality of local parameters by using the plurality of local parameters.
 2. The method of claim 1, wherein the at least one bias is determined based on a sign of a gradient of the local parameter in which each of the at least one bias is included, wherein obtaining the integrated parameter includes: determining a sign value of each of a plurality of gradients included in the plurality of local parameters by using at least one removal parameter included in the plurality of local parameters, and wherein removing the at least one bias includes removing the at least one bias based on a sign value of each of the plurality of gradients.
 3. The method of claim 2, wherein determining the sign value includes: determining the sign value of each of the plurality of gradients based on a number of at least one removal parameters.
 4. The method of claim 3, wherein determining the sign value includes: determining the number of at least one removal parameters based on a number of local parameters having a positive value among the plurality of local parameters.
 5. The method of claim 4, wherein the at least one parameter is determined based on at least one of a number of the plurality of terminals and a transmission power of the plurality of terminals.
 6. A base station for performing a federated learning in a wireless communication system, comprising: a communication module configured to receive a plurality of local parameters from a plurality of terminals; and a processor configured to obtain an integrated parameter based on the plurality of local parameters and transmitting the integrated parameter to the plurality of terminals, wherein the processor is configured to remove at least one bias included in the plurality of local parameters by using the plurality of local parameters.
 7. The base station of claim 6, wherein the at least one bias is determined based on a sign of a gradient of the local parameter in which each of the at least one bias is included, wherein the processor is configured to: determine a sign value of each of a plurality of gradients included in the plurality of local parameters by using at least one removal parameter included in the plurality of local parameters, and remove the at least one bias based on a sign value of each of the plurality of gradients.
 8. The base station of claim 7, wherein the processor is configured to: determine the sign value of each of the plurality of gradients based on a number of at least one removal parameters.
 9. The base station of claim 8, wherein the processor is configured to: determine the number of at least one removal parameters based on a number of local parameters having a positive value among the plurality of local parameters.
 10. The base station of claim 9, wherein the at least one parameter is determined based on at least one of a number of the plurality of terminals and a transmission power of the plurality of terminals.
 11. A base station comprising: one or more transceivers; one or more processors; and one or more memories connected to the one or more processors and configured to store instructions, wherein the instructions are executed by the one or more processors such that the one or more processors support operations for the base station to perform a federated learning, wherein the operations include: performing, with a plurality of terminals, a random access procedure for performing the federated learning; wherein the random access procedure comprises: receiving, from the plurality of terminals, a random access preamble; transmitting, to the plurality of terminals, a random access response; receiving, from the plurality of terminals, a connection request message from based on the random access response; and transmitting, to the plurality of terminals, a contention resolution message; receiving, from the plurality of terminals, a plurality of local parameters; obtaining an integrated parameter based on the plurality of local parameters; and transmitting, to the plurality of terminals, the integrated parameter, wherein obtaining the integrated parameter includes: removing at least one bias included in the plurality of local parameters by using the plurality of local parameters. 